Head Tumor Segmentation and Detection Based on Resunet

Deep learning algorithms have demonstrated remarkable efficacy in the medical imaging field, particularly when it comes to segmentation and classification of brain tumors. This algorithm is being trained and tested using the MRI brain tumor segmentation dataset provided by Kaggle. ResNet50 is used for tumor classification tasks due to its powerful feature extraction capability. Through its deep residual structure, ResNet50 can effectively extract different lesion features and improve classification accuracy. At the same time, ResUNet combines the feature extraction capabilities of ResNet with the segmentation advantages of U-Net, accurately capturing tumor boundaries and achieving high-quality tumor segmentation. For the MRI dataset, the accurate segmentation rate of the model is 91.68%, which provides a certain reference for the medical field. In summary, this study validates the integration of ResUNet50 and ResNet for precis brain tumor detection, contributing to advancements in automated medical image classification analysis. In the field of medical image analysis, accurate brain tumor segmentation and classification are crucial for clinical diagnosis and treatment planning, and provide new ideas and methods for future medical image analysis and clinical applications.